All experiments were performed using the ASVspoof 2019 Logical Access (LA) database [1]} which comprises three independent partitions: train, development and evaluation.
Each partition is used in the same way reported in [2]}. During architecture search, network parameters are updated using 50% of the bona fide utterances and 50% of the spoofed utterances in the training partition. Remaining data is used to update architecture parameters. The cell architectures are selected from those which give the best classification accuracy for the full development partition. During the train from scratch stage, all network parameters, except those of the first convolutional layer, are updated using the full training partition and the best model is selected according to that which gives the best classification accuracy for the full development partition. We report results according to two different metrics: the pooled minimum normalised tandem detection cost function (min-tDCF) [3]}; the pooled equal error rate (EER).
